Multidimensional Poverty and Risk of Atherosclerotic Cardiovascular Disease

Background Poverty is associated with atherosclerotic cardiovascular disease (ASCVD). While poverty can be evaluated using income, a unidimensional poverty metric inadequately captures socioeconomic adversity. Objectives The aim of the study was to examine the association between a multidimensional poverty measure and ASCVD. Methods Survey data from the National Health Interview Survey was analyzed. Four poverty dimensions were used: income, education, self-reported health, and health insurance status. A weighted deprivation score (ci) was calculated for each person. The multidimensional poverty index was computed for various cutoffs, k, for total population, and by ASCVD status. The association between multidimensional poverty and ASCVD was examined using Poisson regression. Area under receiver operator characteristics curve analysis was performed to compare the multidimensional poverty measure with the income poverty measure as a classification tool for ASCVD. Results Among the 328,164 participants, 55.0% were females, the mean age was 46.3 years, 63.1% were non-Hispanic Whites, and 14.1% were non-Hispanic Blacks. Participants with ASCVD (7.95%) experienced greater deprivation at each multidimensional poverty cutoff, k, compared to those without ASCVD. In adjusted models, higher burden of multidimensional poverty was associated with up to 2.4-fold increased prevalence of ASCVD (ci = 0.25, adjusted prevalence ratio [aPR] = 1.66, P < 0.001; ci = 0.50, aPR = 1.99; ci = 0.75, aPR = 2.29; P < 0.001; ci = 1.00, aPR = 2.38, P < 0.001). Multidimensional poverty exhibited modestly higher discriminant validity, compared to income poverty (area under receiver operator characteristics = 0.62 vs 0.58). Conclusions There is an association between the multidimensional poverty and ASCVD. Multidimensional poverty index demonstrates slightly better discriminatory power than income alone. Future validation studies are warranted to redefine poverty's role in health outcomes.

2][3] Traditionally, poverty has been evaluated using monetary approach, which measures it as a shortfall in income/consumption from some poverty line. 4However, poverty is a state in which people are exposed to 'clustered disadvantages, 5 including homelessness, unemployment, violence, and health catastrophes, among others.Hence, a unidimensional poverty metric inadequately captures the true extent of socioeconomic adversity experienced by individuals with CVD.Amartya Sen's capabilities approach presents an alternative paradigm capturing poverty's multidimensional nature, rejecting income as the sole measure of welfare, and emphasizing the expansion of capabilities for human development. 6It focuses on indicators that reflect the freedom to live a "valued life."Poverty, in Sen's framework, signifies the failure to achieve basic capabilities, which are crucial functionings at a minimally adequate level.
Incorporating Sen's approach, multidimensional poverty measures encompassing monetary and nonmonetary dimensions of socioeconomic wellbeing can better identify individuals with CVD, surpassing income-based metrics.Given the substantial medical expenses associated with established CVD, 7 such measures are crucially needed but currently lacking.
Alkire and Foster 8 operationalized Sen's theoretical framework through Alkire-Foster Counting methodology, which measures multidimensional poverty in national and global context.To the best of our knowledge, no large-scale, population-based studies in the United States have used the method to examine the association between a comprehensive multidimensional poverty index (MPI)-inclusive of income, education, insurance status, and selfreported health-and atherosclerotic CVD (ASCVD).
To address this knowledge gap, we used the Alkire-Foster Counting methodology to: 1) compute multidimensional poverty estimates for the general population and in adults with and without ASCVD; 2) examine the association between multidimensional poverty and ASCVD; and 3) extend previously published analysis by Alkire and Foster Butt et al We reported summary statistics of the de- We compared the discriminant validity of multidimensional poverty with income-based poverty using receiver-operating characteristics (ROC) curves.
The ROC curve for multidimensional poverty was plotted using the weighted deprivation score variable, while that for income poverty was plotted using the income-poverty threshold ratio. 12 additional analysis, we decomposed MPI estimate obtained at k ¼ 0.50 8 by race and ethnicity for participants with and without ASCVD to understand potential variation in multidimensional poverty characteristics for these population subgroups.
Decomposition method is described in detail elsewhere.of the poverty cutoff.This suggests that ASCVD may be associated with a greater prevalence of povertyrelated factors.The intensity of poverty (A) is relatively similar between the ASCVD and non-ASCVD populations, especially at lower poverty cutoffs.
This implies that while the prevalence of poverty is higher in the ASCVD population, the severity of poverty experienced by the poor in both groups is comparable.

higher MPI contribution of education (versus income)
for Hispanic adults without ASCVD.This indicates that addressing disparities in these dimensions could be crucial in reducing multidimensional poverty and its impact on ASCVD risk across different racial and ethnic populations.iable analyses, we observed that a higher burden of simultaneous deprivations in poverty dimensions was associated with a higher prevalence of ASCVD, independent of clinical and demographic factors.
Moreover, multidimensional poverty was found to have modestly higher discriminatory power for The multidimensional weighted deprivation score (ci) is computed using the Alkire-Foster method.This method considered dimensions such as income, education, self-reported health, and health insurance status to capture poverty.Each dimension was assigned an equal weight of 1/4, denoting their relative importance.Our results are an important addition to the limited contemporary discourse on poverty in the CVD space.
Existing literature is replete with traditional incomebased measure of poverty and its inverse relationship with CVD risk factors and outcomes, 3,14,15 or with reports of association between other individual dimensions of MPI (self-reported health, [16][17][18] insurance, 19 educational attainment) 1,2,14 and CVD outcomes.However, no large-scale study in the U.S. For public health policy, the results underscore the need to prioritize health equity by addressing systemic barriers that contribute to these disparities.
Policymakers should collaborate with health care providers, community organizations, and other stakeholders to develop culturally sensitive, targeted programs addressing income, education, and health care access.
This paper demonstrates that greater socioeconomic adversity, highlighted by higher multidimensional poverty burden, was significantly associated with a higher prevalence of ASCVD.Our finding is consistent with recent evidence that looked at socioeconomic indicators, other social determinants of health (SDOH), and CVD risk factors and outcomes. 21,22Differences in income and other socioeconomic determinants are consistently linked to poor cardiovascular health and are major drivers of racial/ethnic disparities in CVD. 3 Various physiologic, psychosocial, and behavioral mechanisms have been examined in scientific literature to establish biological plausibility for the observed associations between poor socioeconomic markers and adverse CVD outcomes. 21,23Hence, this calls for greater focus on addressing these and related SDOH in the ASCVD population through targeted policy interventions to mitigate disease burden.Further, the interdependent nature of individual dimensions of poverty (such as income, education, and financial burden of health care) has been demonstrated extensively in the literature. 24,25In this study, we reported the effects of individual poverty domains on ASCVD; future work should build on these findings and further explore  This can help ensure that providers are equipped to STUDY LIMITATIONS.This study is not without limitations.First, the cross-sectional nature of NHIS precludes an assessment of causal relationship between multidimensional poverty and ASCVD.Reverse causality between poverty and ASCVD cannot be definitively assessed in this study.Longitudinal research designs would be valuable in examining how changes in poverty dimensions over time impact the risk of CVD.Second, our findings are based on self-reported data, which may be subject to recall and reporting biases.While the NHIS undergoes quality checks and has demonstrated good correlation with clinically ascertained data, this inherent limitation should be acknowledged. 27Third, inclusion of self-reported health as dimension of poverty could introduce confounding in our analysis.To address this concern, we conducted a sensitivity analysis by excluding selfreported health from the index.The results (resented in Supplemental

CONCLUSIONS
Individuals with ASCVD face a greater burden of multidimensional poverty than those without ASCVD.
Our findings reveal that, compared to income poverty, multidimensional poverty is a stronger predictor of ASCVD.Hence, based solely on income, poverty may be insufficient for identifying individuals at a higher knasir@houstonmethodist.org.

R E F E R E N C E S
8 from the year 2004 to the years 2007 to 2018 using the same data (National Health Interview Survey [NHIS]).We also compared the discriminatory ability of multidimensional poverty vs unidimensional income-based poverty for the classification of prevalent ASCVD.METHODS DATA SOURCE AND STUDY DESIGN.This study used pooled data from respondents of the NHIS from 2007 to 2018. 9NHIS is an annual household interview survey of the noninstitutionalized U.S. civilian population conducted by the National Center for Health Statistics.Respondents, sampled using a complex multistage area probability design, report the following information: demographic and relationship information about all persons in the household (Household Composition Core); health status, health care access, and utilization for each family in the household (Family Core); and further information from a child and adult selected from each family (Sample Child and Sample Adult Cores).We used the NHIS Sample Adult Core file with supplementation of variables from Household Composition and the Family Core files.NHIS data are publicly available and deidentified; hence, this study was exempt from the purview of the Houston Methodist's Institutional Review Board.STUDY VARIABLES.M u l t i d i m e n s i o n a l p o v e r t y .Multidimensional poverty, estimated using the Alkire-Foster counting methodology (described in detail elsewhere 8 ), was the primary independent variable of interest.It involves constructing a weighted deprivation score (c i ) at the individual level, which is then used to calculate the MPI at the population level.W e i g h t e d d e p r i v a t i o n s c o r e ( c i ) .The Alkire-Foster method identifies a set of dimensions (d), which are different aspects of well-being that are important for a person's overall quality of life.An individual is said to experience poverty in a dimension if their dimensional achievement is below a specified deprivation cutoff level.Based on Alkire and Foster, 8 we included income, education, selfreported health, and health insurance status as dimensions of poverty.Deprivation cutoffs were defined as household income below the federal poverty level, less than high school education, 'fair' or 'poor' self-reported health, and no health insurance, respectively.All dimensions were assigned an equal weight of 1 / 4 , representing their relative importance, such that the sum of all weights equals 1.The weighted deprivation score (c i ) is computed by summing the weighted dimensions for each individual.The score reflects the proportion of simultaneous dimensional deprivations experienced by each person.Thus, our weighted deprivation score variable had values of 0 (no deprivation in any dimension), 0.25 (deprived in 1 dimension), 0.50 (deprived in A B B R E V I A T I O mographic and clinical characteristics for the total study population (ie, all NHIS 2007-2018 participants meeting the study inclusion criteria) and for persons with and without ASCVD separately.Chi-squared tests were used to compare differences between ASCVD and non-ASCVD population subgroups.We estimated MPI, H, and A for the total study population as well as by ASCVD status, using 4 poverty cutoffs: k (0.25, 0.50, 0.75, and 1.00).All multidimensional poverty estimates along with their measures of accuracy and P values were obtained using the Bootstrap methodology with 1,000 replications of sampling with data replacement.The weighted deprivation score c i was defined as a measure for multidimensional poverty burden among individual participants.We used Poisson regression to generate prevalence ratios (PR) of the association between multidimensional poverty and prevalent ASCVD along with P values and 95% CI.Poisson regression with robust variance provides correct estimates in cross-sectional studies, where exposure and outcome are measured at the same point in time. 11Three models were generated: an unadjusted model (Model 1); a model adjusted for age, sex, and race and ethnicity (Model 2); and a fully adjusted model (Model 3) including cardiovascular risk factors, comorbidities, and all variables in Model 2.

Figure 1
(ROC)  compares the performance of the multidimensional poverty measure in classifying ASCVD relative to income poverty.The area under curve for multidimensional poverty (0.62 AE 0.002) was modestly higher than that for the income poverty (0.58; S.E: 0.002), suggesting potentially higher discriminant validity of the former (versus the latter) as an ASCVD classification tool.DISTRIBUTION OF MPI BY RACE AND ETHNICITYSUBGROUPS AND ASCVD STATUS.Table4presents distribution of MPI by race and ethnicity for individuals with and without ASCVD sub-groups at a poverty cutoff of 0.5.The data reveals significant disparities in MPI across different racial and ethnic groups.Overall, regardless of ASCVD status, the degree of multidimensional poverty (MPI) was greatest among Hispanic persons followed by non-Hispanic Black individuals.Despite the relatively small proportion of Hispanic and non-Hispanic Black population in the total population (ASCVD: 10.4% and 15.2%; non-ASCVD: 16.6% and 13.9%), both of these subgroups contribute disproportionately higher toward MPI (ASCVD: 21.5% and 25.0%; non-ASCVD: 40.4% and 20.8%).

Figure 2
Figure2illustrates the dimensional breakdown of the MPI for each race and ethnicity subgroup for adults with and without ASCVD.These results show that the distribution of the percentage contribution of each dimension to the MPI varies across different race and ethnicity subgroups.In general, self-reported health and education tend to have higher contributions to the MPI, especially among adults with ASCVD, whereas income was the primary contributor in the non-ASCVD population.This pattern was observed for all racial/ethnic groups, except for the

Figure 3
Figure 3 reports MPI levels computed by race and ethnicity subgroups for all values of k for participants with and without ASCVD.For any given poverty cutoff (k), individuals with ASCVD experience greater burden of multidimensional poverty compared to those without ASCVD.Additionally, consistent with the findings reported above, Hispanic and non-Hispanic Black individuals experienced higher burden of multidimensional poverty at any given poverty cutoff relative to non-Hispanic White individuals for both ASCVD and non-ASCVD populations, except for k ¼ 1.00 (owing to the low sample size for this category).The distribution of multidimensional poverty by individual MPI dimensions and association with ASCVD is depicted in the Central Illustration.

FIGURE 1 4 Multidimensional
FIGURE 1 Analysis of Discriminative Ability (AUROC) for Prediction of Prevalent ASCVD has examined the aggregated effect of the independent social risk factors on ASCVD using a robust composite index of poverty that captures monetary and nonmonetary dimensions.One notable mention is Callander et al20 who constructed the 'Freedom Poverty Measure' and analyzed multiple forms of disadvantage experienced by those with no health condition, CVD, and all other health conditions in a cross-sectional study design.The Freedom Index was designed to assess the impact of government policy on the well-being of Australians, which may limit its applicability to the broader study of ASCVD outcomes and racial/ethnic disparities in different populations.The novelty of our study is the application of Alkire-Foster MPI, a validated global measure, to assess the association of multidimensional poverty with prevalent CVD on a large population scale in the United States.The MPI framework is flexible, allowing for the inclusion of context-specific indicators, but it also maintains a core set of dimensions, such as health, education, and living standards, that are relevant and comparable across different populations.Furthermore, our study delved deeper into the disparities among different racial and ethnic groups in terms of multidimensional poverty and its impact on ASCVD outcomes.By providing a detailed breakdown of MPI values and percentage contributions for different racial/ethnic groups within ASCVD and non-ASCVD populations, our research has highlighted the inequities that exist among these groups and their potential influence on ASCVD outcomes.In the context of disparities research, these findings highlight the importance of considering multidimensional poverty as a key factor driving health disparities.Future studies should further explore the interplay between race and ethnicity, social determinants, and health outcomes to inform effective interventions.

JMultidimensional
A C C : A D V A N C E S , V O L . 3 , N O .7 Poverty and ASCVD in the US intersectionality among the individual poverty domains to affect the risk of CVD.Our study has important clinical implications as it highlights the need for a holistic approach to patient care that considers medical factors and SDOH, such as income, education, and self-reported health.Clinical practice should consider incorporating targeted interventions for at-risk populations facing multidimensional poverty and higher ASCVD risk, including culturally sensitive education and improved health care access.The MPI can be used as a tool to "flag" socially vulnerable individuals experiencing the negative consequences of socioeconomic inequities in the health care system.Furthermore, hospitals and health care providers should work toward designing cross-sectoral collaboration with social services, education, and community organizations to address broader health disparities and create comprehensive support systems that address persistent structural barriers. 26Prioritizing health equity in clinical practices, addressing implicit biases, and providing cultural competency training can ensure equal access to quality care for all patients.Hospitals should invest in continuous education and training for their health care providers, ensuring that they are up-to-date on the latest research and best practices for addressing multidimensional poverty and its impact on ASCVD.

FIGURE 2
FIGURE 2 Dimensional Deprivation Decomposition of MPI by Poverty Dimensions

FIGURE 3 and
FIGURE 3 Multidimensional Poverty Index (MPI) for Race and Ethnicity Groups by Poverty Cutoff, k

4 Multidimensional
CENTRAL ILLUSTRATION Multidimensional Poverty and Risk of Atherosclerotic Cardiovascular DiseaseButt SA, et al.JACC Adv.2024;3(7):100928.A higher burden of multidimensional poverty is associated with up to 2.0-fold increased prevalence of ASCVD.The multidimensional weighted deprivation score (ci) considers dimensions such as income, education, self-reported health, and health insurance status to capture poverty.The ci has values from 0 to 1: 0 (no deprivation in any dimension), 0.25 (deprived in any 1 dimension), 0.50 (deprived in 2 dimensions), 0.75 (deprived in 3 dimensions), and 1.00 (deprived in all 4 dimensions).ASCVD ¼ atherosclerotic cardiovascular disease.Butt et alJ A C C : A D V A N C E S , V O L . 3 , N O .7 , 2 0 2 Poverty and ASCVD in the US J U L Y 2 0 2 4 : 1 0 0 9 2 8 risk of ASCVD.Recognizing the importance of multidimensional poverty in relation to ASCVD risk can help health care professionals and policymakers in designing targeted interventions.The results also highlight the need for further research into the relationship between multidimensional poverty and health disparities, particularly in the context of ASCVD.This could unveil new insights into the complex interplay between various socioeconomic factors and cardiovascular health outcomes.ACKNOWLEDGMENT The authors thank Jacob M. Kolman, MA, ISMPP, CMPP, Senior Scientific Writer at the Houston Methodist Academic Institute, for language editing, formatting, and critical review of the manuscript.FUNDING SUPPORT AND AUTHOR DISCLOSURES Dr Nasir is on the advisory board of Amgen and Novartis, and his research is partly supported by the Jerold B. Katz Academy of Translational Research.All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.ADDRESS FOR CORRESPONDENCE: Dr Khurram Nasir, Division of Cardiovascular Prevention and Wellness, Department of Cardiology, Houston Methodist DeBakey Heart & Vascular Center, 6550 Fannin St Suite 1801, Houston, Texas 77030, USA.E-mail:

Table 2
presents the population-level distribution of MPI for the overall study population as well as by ASCVD status for various values of multidimensional poverty cutoff (k).We observed that as k increased from 0.25 to 1, as expected, the headcount ratio of driven by greater values of H in the ASCVD group for all values of k (k ¼ 0.25, H: 59.5% vs 38.1%; k ¼ 0.50, H: 26.4% vs 14.2%; k ¼ 0.75, H: 8.02% vs 3.91%; k ¼ 1.00, H: 0.50% vs 0.38%).The headcount ratios (H) also show that a higher proportion of the ASCVD population is considered multidimensionally poor compared to the non-ASCVD population, regardless

TABLE 1
Characteristics of Participants From the National Health Interview Survey

Table 3
DISCRIMINANT VALIDITY OF THE MPI.

TABLE 2
Population-Level MPI Estimates, Overall and by ASCVD Status, for Various Poverty CutoffsThe multidimensional poverty cutoff (k) is a threshold used to determine poverty status in the calculation of the multidimensional poverty index, based on the Alkire-Foster method.It represents the minimum number of weighted deprivations required across various indicators to classify an individual as multidimensionally poor.The poverty cutoff (k) values, such as k ¼ 0.25 indicates deprivation in at least 25% of the indicators, k ¼ 0.50 signifies deprivation in at least 50% of the indicators, and so on.If an individual's weighted deprivation score (ci) is equal to or greater than poverty cutoff (k), they are classified as multidimensionally poor.
aEstimates are significant at P < 0.001.bP value reported for the test of significance of difference (t-test) of MPI estimates for ASCVD and non-ASCVD population subgroups.cBootstrap method was used to obtain 95% CI for MPI and H & A estimates.A ¼ multidimensional poverty intensity; ASCVD ¼ atherosclerotic cardiovascular disease; H ¼ multidimensional poverty headcount ratio; k ¼ multidimensional poverty cutoff; MPI ¼ multidimensional poverty index.

TABLE 3
Association Between the Multidimensional Poverty Weighted Deprivation Score (c i ) and Prevalent ASCVD The weighted deprivation score was obtained by summing the products of the weights and dimensional deprivations for each person.Hence, the ci represents the weighted proportion of simultaneous deprivations across multiple dimensions of poverty experienced by each individual.It has values of 0 (no deprivation in any dimension), 0.25 (deprived in any 1 dimension), 0.50 (deprived in 2 dimensions), 0.75 (deprived in 3 dimensions), and 1.00 (deprived in all 4 dimensions), respectively.a Model 1 ¼ Unadjusted.b Model 2 ¼ Adjusted for age, sex, and ethnicity/race.c Model 3 ¼ Adjusted for Model 2 þ cardiovascular risk factors profile þ comorbidities.

TABLE 4
Distribution of Multidimensional Poverty Index by Race and Ethnicity by ASCVD ¼ 0.50.a Estimates are significant at P < 0.001.b Bootstrap method was used to obtain 95% CI for MPI estimates.ASCVD ¼ atherosclerotic cardiovascular disease; H ¼ multidimensional poverty headcount ratio; MPI ¼ multidimensional poverty index; NH ¼ non-Hispanic.

Table 2
The weight affixed to each dimension reflects the normative value that a deprivation in that dimension has for poverty, relative to deprivations in the other dimensions.However, universal weights don't exist, and differential weights challenge the reliability of poverty measurement.Hence, consistent with Alkire